Groningen Growth and Development Centre
Bibliographic record
Abstract
This report is a feasibility study into an international productivity project using the KLEM growth accounting methodology. It could not have been written without the help of many people. Thanks are due to Bart van Ark, Robert McGuckin, Mary O'Mahony and Dirk Pilat who provided me with helpful comments on earlier drafts of this report. During my visit to the Kennedy School of Government, Harvard University, Mun Ho, Kevin Stiroh and Dale Jorgenson provided me with many insights into the theories and empirical applications of the KLEM-methodology. Discussions with Frank Lee, Wulong Gu and Jianmin Tang (Industry Canada), René Durand (Statistics Canada) and Svend E. Hougaard Jensen, Anders Sørensen, Mogens Fosgerau and Steffen Andersen of the Center of Economic and Business Research (CEBR) provided further insights and new ideas. At the meeting of the KLEM-research consortium on 9 and 10 December at the Centre of Economic and Business Research (CEBR), Copenhagen, consortium members provided me with relevant information, references on ongoing projects and helpful suggestions.Their help is gratefully acknowledged. Prasada Rao (University of New England) is thanked for his advice on the section on purchasing power parities. Finally, the Conference Board is acknowledged for financing the preparation
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.366 | 0.179 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".